Overview

Dataset statistics

Number of variables29
Number of observations1044184
Missing cells1324790
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 GiB
Average record size in memory1.1 KiB

Variable types

Categorical17
Numeric12

Alerts

settlementdate has a high cardinality: 789 distinct valuesHigh cardinality
BMU ID has a high cardinality: 457 distinct valuesHigh cardinality
BMU Party ID has a high cardinality: 125 distinct valuesHigh cardinality
BMU Party Name has a high cardinality: 125 distinct valuesHigh cardinality
acceptedprice is highly overall correlated with acceptedvolumeHigh correlation
acceptedvolume is highly overall correlated with acceptedprice and 1 other fieldsHigh correlation
LOC LAT is highly overall correlated with LOC LONG and 5 other fieldsHigh correlation
LOC LONG is highly overall correlated with LOC LAT and 3 other fieldsHigh correlation
LOC Center LAT is highly overall correlated with LOC Center LONG and 2 other fieldsHigh correlation
LOC Center LONG is highly overall correlated with LOC Center LAT and 2 other fieldsHigh correlation
Transmission Loss Factor is highly overall correlated with LOC LAT and 3 other fieldsHigh correlation
Generation Capacity is highly overall correlated with Demand Capacity and 3 other fieldsHigh correlation
Demand Capacity is highly overall correlated with Generation Capacity and 1 other fieldsHigh correlation
recordtype is highly overall correlated with acceptedvolume and 1 other fieldsHigh correlation
BMU Type is highly overall correlated with Trading Unit and 3 other fieldsHigh correlation
BMU Fuel Type is highly overall correlated with recordtype and 3 other fieldsHigh correlation
BMU GSP Group Id is highly overall correlated with LOC LAT and 4 other fieldsHigh correlation
BMU GSP Group Name is highly overall correlated with LOC LAT and 4 other fieldsHigh correlation
GSP LOC Center is highly overall correlated with LOC Center LAT and 2 other fieldsHigh correlation
Trading Unit is highly overall correlated with LOC LAT and 15 other fieldsHigh correlation
PC Flag is highly overall correlated with LOC LAT and 3 other fieldsHigh correlation
PC Status is highly overall correlated with Generation Capacity and 5 other fieldsHigh correlation
Exempt Export Flag is highly overall correlated with BMU Type and 2 other fieldsHigh correlation
Base TU Flag is highly overall correlated with Generation Capacity and 5 other fieldsHigh correlation
FPN Flag is highly overall correlated with PC FlagHigh correlation
BMU Type is highly imbalanced (54.4%)Imbalance
FPN Flag is highly imbalanced (95.0%)Imbalance
Trading Unit has 416890 (39.9%) missing valuesMissing
PC Flag has 907900 (86.9%) missing valuesMissing
acceptedprice is highly skewed (γ1 = 103.5457832)Skewed
acceptedprice has 58177 (5.6%) zerosZeros
Generation Capacity has 23453 (2.2%) zerosZeros
Demand Capacity has 206022 (19.7%) zerosZeros

Reproduction

Analysis started2023-06-05 09:49:21.669186
Analysis finished2023-06-05 09:52:26.992566
Duration3 minutes and 5.32 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

recordtype
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.5 MiB
BID
654268 
OFFER
389916 

Length

Max length5
Median length3
Mean length3.7468339
Min length3

Characters and Unicode

Total characters3912384
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBID
2nd rowBID
3rd rowBID
4th rowBID
5th rowBID

Common Values

ValueCountFrequency (%)
BID 654268
62.7%
OFFER 389916
37.3%

Length

2023-06-05T09:52:27.291105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T09:52:28.094437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bid 654268
62.7%
offer 389916
37.3%

Most occurring characters

ValueCountFrequency (%)
F 779832
19.9%
B 654268
16.7%
I 654268
16.7%
D 654268
16.7%
O 389916
10.0%
E 389916
10.0%
R 389916
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3912384
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 779832
19.9%
B 654268
16.7%
I 654268
16.7%
D 654268
16.7%
O 389916
10.0%
E 389916
10.0%
R 389916
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3912384
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 779832
19.9%
B 654268
16.7%
I 654268
16.7%
D 654268
16.7%
O 389916
10.0%
E 389916
10.0%
R 389916
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3912384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 779832
19.9%
B 654268
16.7%
I 654268
16.7%
D 654268
16.7%
O 389916
10.0%
E 389916
10.0%
R 389916
10.0%

settlementdate
Categorical

Distinct789
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.7 MiB
2022-06-11
 
4431
2022-10-06
 
4337
2022-11-10
 
3968
2022-06-12
 
3547
2021-11-18
 
3538
Other values (784)
1024363 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10441840
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-01
2nd row2021-01-01
3rd row2021-01-01
4th row2021-01-01
5th row2021-01-01

Common Values

ValueCountFrequency (%)
2022-06-11 4431
 
0.4%
2022-10-06 4337
 
0.4%
2022-11-10 3968
 
0.4%
2022-06-12 3547
 
0.3%
2021-11-18 3538
 
0.3%
2021-02-19 3526
 
0.3%
2021-02-23 3482
 
0.3%
2022-10-09 3465
 
0.3%
2022-01-01 3336
 
0.3%
2021-02-21 3267
 
0.3%
Other values (779) 1007287
96.5%

Length

2023-06-05T09:52:28.365198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-06-11 4431
 
0.4%
2022-10-06 4337
 
0.4%
2022-11-10 3968
 
0.4%
2022-06-12 3547
 
0.3%
2021-11-18 3538
 
0.3%
2021-02-19 3526
 
0.3%
2021-02-23 3482
 
0.3%
2022-10-09 3465
 
0.3%
2022-01-01 3336
 
0.3%
2021-02-21 3267
 
0.3%
Other values (779) 1007287
96.5%

Most occurring characters

ValueCountFrequency (%)
2 3219333
30.8%
0 2317506
22.2%
- 2088368
20.0%
1 1469694
14.1%
3 312906
 
3.0%
6 185730
 
1.8%
9 176718
 
1.7%
7 173642
 
1.7%
8 172007
 
1.6%
5 165714
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8353472
80.0%
Dash Punctuation 2088368
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3219333
38.5%
0 2317506
27.7%
1 1469694
17.6%
3 312906
 
3.7%
6 185730
 
2.2%
9 176718
 
2.1%
7 173642
 
2.1%
8 172007
 
2.1%
5 165714
 
2.0%
4 160222
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 2088368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10441840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3219333
30.8%
0 2317506
22.2%
- 2088368
20.0%
1 1469694
14.1%
3 312906
 
3.0%
6 185730
 
1.8%
9 176718
 
1.7%
7 173642
 
1.7%
8 172007
 
1.6%
5 165714
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10441840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3219333
30.8%
0 2317506
22.2%
- 2088368
20.0%
1 1469694
14.1%
3 312906
 
3.0%
6 185730
 
1.8%
9 176718
 
1.7%
7 173642
 
1.7%
8 172007
 
1.6%
5 165714
 
1.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.7 MiB
2022
484465 
2021
474003 
2023
85716 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4176736
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2022 484465
46.4%
2021 474003
45.4%
2023 85716
 
8.2%

Length

2023-06-05T09:52:28.617232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T09:52:28.882765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2022 484465
46.4%
2021 474003
45.4%
2023 85716
 
8.2%

Most occurring characters

ValueCountFrequency (%)
2 2572833
61.6%
0 1044184
25.0%
1 474003
 
11.3%
3 85716
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4176736
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2572833
61.6%
0 1044184
25.0%
1 474003
 
11.3%
3 85716
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4176736
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2572833
61.6%
0 1044184
25.0%
1 474003
 
11.3%
3 85716
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4176736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2572833
61.6%
0 1044184
25.0%
1 474003
 
11.3%
3 85716
 
2.1%

settlementdate_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2551916
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2023-06-05T09:52:29.095614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7070542
Coefficient of variation (CV)0.5926364
Kurtosis-1.4017202
Mean6.2551916
Median Absolute Deviation (MAD)4
Skewness0.034206517
Sum6531571
Variance13.742251
MonotonicityNot monotonic
2023-06-05T09:52:29.371436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 136503
13.1%
1 123805
11.9%
11 106836
10.2%
10 98299
9.4%
12 80393
7.7%
6 76436
7.3%
3 75025
7.2%
8 73766
7.1%
7 72882
7.0%
9 71851
6.9%
Other values (2) 128388
12.3%
ValueCountFrequency (%)
1 123805
11.9%
2 136503
13.1%
3 75025
7.2%
4 60734
5.8%
5 67654
6.5%
6 76436
7.3%
7 72882
7.0%
8 73766
7.1%
9 71851
6.9%
10 98299
9.4%
ValueCountFrequency (%)
12 80393
7.7%
11 106836
10.2%
10 98299
9.4%
9 71851
6.9%
8 73766
7.1%
7 72882
7.0%
6 76436
7.3%
5 67654
6.5%
4 60734
5.8%
3 75025
7.2%

settlementdate_day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.585977
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2023-06-05T09:52:29.634464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6495811
Coefficient of variation (CV)0.55495918
Kurtosis-1.1634211
Mean15.585977
Median Absolute Deviation (MAD)7
Skewness0.033165395
Sum16274628
Variance74.815253
MonotonicityNot monotonic
2023-06-05T09:52:29.884360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
11 41509
 
4.0%
10 39335
 
3.8%
6 38612
 
3.7%
9 38535
 
3.7%
25 38095
 
3.6%
20 37934
 
3.6%
19 37631
 
3.6%
18 36063
 
3.5%
17 35871
 
3.4%
16 35478
 
3.4%
Other values (21) 665121
63.7%
ValueCountFrequency (%)
1 31913
3.1%
2 32738
3.1%
3 34981
3.4%
4 32151
3.1%
5 29228
2.8%
6 38612
3.7%
7 34703
3.3%
8 35015
3.4%
9 38535
3.7%
10 39335
3.8%
ValueCountFrequency (%)
31 18605
1.8%
30 31222
3.0%
29 28701
2.7%
28 27163
2.6%
27 30186
2.9%
26 35204
3.4%
25 38095
3.6%
24 35455
3.4%
23 35357
3.4%
22 30891
3.0%

settlementperiod
Real number (ℝ)

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.312177
Minimum1
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2023-06-05T09:52:30.158355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median25
Q337
95-th percentile46
Maximum48
Range47
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.415408
Coefficient of variation (CV)0.52999821
Kurtosis-1.1432565
Mean25.312177
Median Absolute Deviation (MAD)11
Skewness-0.06490988
Sum26430570
Variance179.97318
MonotonicityNot monotonic
2023-06-05T09:52:30.450719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
15 27463
 
2.6%
34 26021
 
2.5%
16 25881
 
2.5%
17 25734
 
2.5%
35 25628
 
2.5%
14 25280
 
2.4%
39 24908
 
2.4%
33 24813
 
2.4%
38 24729
 
2.4%
36 24707
 
2.4%
Other values (38) 789020
75.6%
ValueCountFrequency (%)
1 19122
1.8%
2 18308
1.8%
3 17776
1.7%
4 17298
1.7%
5 16980
1.6%
6 16753
1.6%
7 16758
1.6%
8 16570
1.6%
9 16912
1.6%
10 17976
1.7%
ValueCountFrequency (%)
48 18864
1.8%
47 21557
2.1%
46 20981
2.0%
45 21113
2.0%
44 21692
2.1%
43 22539
2.2%
42 22482
2.2%
41 23375
2.2%
40 23881
2.3%
39 24908
2.4%

BMU ID
Categorical

Distinct457
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.5 MiB
T_MRWD-1
 
24705
T_WBURB-2
 
18688
T_GRAI-8
 
17905
T_WBURB-1
 
17230
T_CARR-2
 
16490
Other values (452)
949166 

Length

Max length11
Median length10
Mean length8.8012889
Min length7

Characters and Unicode

Total characters9190165
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE_GYAR-1
2nd rowE_SHOS-1
3rd rowT_CDCL-1
4th rowT_MRWD-1
5th rowT_PEMB-11

Common Values

ValueCountFrequency (%)
T_MRWD-1 24705
 
2.4%
T_WBURB-2 18688
 
1.8%
T_GRAI-8 17905
 
1.7%
T_WBURB-1 17230
 
1.7%
T_CARR-2 16490
 
1.6%
T_CDCL-1 15535
 
1.5%
T_CARR-1 15286
 
1.5%
T_SHBA-1 14702
 
1.4%
T_SEAB-1 13808
 
1.3%
T_GRAI-6 13125
 
1.3%
Other values (447) 876710
84.0%

Length

2023-06-05T09:52:30.793682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
t_mrwd-1 24705
 
2.4%
t_wburb-2 18688
 
1.8%
t_grai-8 17905
 
1.7%
t_wburb-1 17230
 
1.7%
t_carr-2 16490
 
1.6%
t_cdcl-1 15535
 
1.5%
t_carr-1 15286
 
1.5%
t_shba-1 14702
 
1.4%
t_seab-1 13808
 
1.3%
t_grai-6 13125
 
1.3%
Other values (447) 876710
84.0%

Most occurring characters

ValueCountFrequency (%)
_ 1175032
 
12.8%
T 990154
 
10.8%
- 888308
 
9.7%
1 616704
 
6.7%
A 469419
 
5.1%
R 456794
 
5.0%
E 412233
 
4.5%
S 333981
 
3.6%
2 321084
 
3.5%
W 302895
 
3.3%
Other values (28) 3223561
35.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5675604
61.8%
Decimal Number 1451221
 
15.8%
Connector Punctuation 1175032
 
12.8%
Dash Punctuation 888308
 
9.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 990154
17.4%
A 469419
 
8.3%
R 456794
 
8.0%
E 412233
 
7.3%
S 333981
 
5.9%
W 302895
 
5.3%
B 293433
 
5.2%
C 291391
 
5.1%
D 277979
 
4.9%
L 246672
 
4.3%
Other values (16) 1600653
28.2%
Decimal Number
ValueCountFrequency (%)
1 616704
42.5%
2 321084
22.1%
0 266218
18.3%
3 108218
 
7.5%
4 53337
 
3.7%
6 29496
 
2.0%
5 27715
 
1.9%
8 18051
 
1.2%
7 10381
 
0.7%
9 17
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1175032
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 888308
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5675604
61.8%
Common 3514561
38.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 990154
17.4%
A 469419
 
8.3%
R 456794
 
8.0%
E 412233
 
7.3%
S 333981
 
5.9%
W 302895
 
5.3%
B 293433
 
5.2%
C 291391
 
5.1%
D 277979
 
4.9%
L 246672
 
4.3%
Other values (16) 1600653
28.2%
Common
ValueCountFrequency (%)
_ 1175032
33.4%
- 888308
25.3%
1 616704
17.5%
2 321084
 
9.1%
0 266218
 
7.6%
3 108218
 
3.1%
4 53337
 
1.5%
6 29496
 
0.8%
5 27715
 
0.8%
8 18051
 
0.5%
Other values (2) 10398
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9190165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 1175032
 
12.8%
T 990154
 
10.8%
- 888308
 
9.7%
1 616704
 
6.7%
A 469419
 
5.1%
R 456794
 
5.0%
E 412233
 
4.5%
S 333981
 
3.6%
2 321084
 
3.5%
W 302895
 
3.3%
Other values (28) 3223561
35.1%

acceptedprice
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct24420
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.00159
Minimum-9999
Maximum99999
Zeros58177
Zeros (%)5.6%
Negative235137
Negative (%)22.5%
Memory size8.0 MiB
2023-06-05T09:52:31.122094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile-74.58
Q10
median88
Q3179
95-th percentile349.9
Maximum99999
Range109998
Interquartile range (IQR)179

Descriptive statistics

Standard deviation214.475
Coefficient of variation (CV)2.062228
Kurtosis45328.406
Mean104.00159
Median Absolute Deviation (MAD)88
Skewness103.54578
Sum1.085968 × 108
Variance45999.526
MonotonicityNot monotonic
2023-06-05T09:52:31.404312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58177
 
5.6%
-56.58 10384
 
1.0%
-72 9866
 
0.9%
-67.13 7987
 
0.8%
50 7359
 
0.7%
-69.49 7287
 
0.7%
-8.52 7283
 
0.7%
-70.97 7179
 
0.7%
150 7081
 
0.7%
-75 6952
 
0.7%
Other values (24410) 914629
87.6%
ValueCountFrequency (%)
-9999 10
< 0.1%
-5024.5 1
 
< 0.1%
-3203.253333 1
 
< 0.1%
-999 2
 
< 0.1%
-500 7
< 0.1%
-285 13
< 0.1%
-252.54 1
 
< 0.1%
-250 5
 
< 0.1%
-221.31 1
 
< 0.1%
-218.86 13
< 0.1%
ValueCountFrequency (%)
99999 1
 
< 0.1%
10000 2
 
< 0.1%
9999 29
< 0.1%
8000 1
 
< 0.1%
6000 2
 
< 0.1%
5500 2
 
< 0.1%
5234.11 1
 
< 0.1%
5000 4
 
< 0.1%
4991.5 1
 
< 0.1%
4950 3
 
< 0.1%

acceptedvolume
Real number (ℝ)

Distinct137775
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.99005659
Minimum-1220.238
Maximum1100
Zeros6468
Zeros (%)0.6%
Negative650084
Negative (%)62.3%
Memory size8.0 MiB
2023-06-05T09:52:31.701596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1220.238
5-th percentile-169.05
Q1-57.4
median-13
Q314.25
95-th percentile280
Maximum1100
Range2320.238
Interquartile range (IQR)71.65

Descriptive statistics

Standard deviation150.36865
Coefficient of variation (CV)-151.87884
Kurtosis11.765228
Mean-0.99005659
Median Absolute Deviation (MAD)33.002
Skewness-0.054798678
Sum-1033801.3
Variance22610.731
MonotonicityNot monotonic
2023-06-05T09:52:31.995371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230 10014
 
1.0%
20 9055
 
0.9%
-25 8179
 
0.8%
-15 7479
 
0.7%
-20 6802
 
0.7%
0 6468
 
0.6%
16 5909
 
0.6%
-40 5535
 
0.5%
-24 5286
 
0.5%
18 4968
 
0.5%
Other values (137765) 974489
93.3%
ValueCountFrequency (%)
-1220.238 1
 
< 0.1%
-1195.762 1
 
< 0.1%
-1180 268
< 0.1%
-1179.984 2
 
< 0.1%
-1179.95 1
 
< 0.1%
-1179.016 3
 
< 0.1%
-1179 21
 
< 0.1%
-1178.984 1
 
< 0.1%
-1178.958 1
 
< 0.1%
-1178.934 1
 
< 0.1%
ValueCountFrequency (%)
1100 1
 
< 0.1%
1066.934 1
 
< 0.1%
1009.334 1
 
< 0.1%
925.832 1
 
< 0.1%
922.98 1
 
< 0.1%
918.316 1
 
< 0.1%
897.334 1
 
< 0.1%
890.334 1
 
< 0.1%
889 1
 
< 0.1%
888 6
< 0.1%

BMU Type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.8 MiB
T
786496 
E
115859 
2
115316 
V
 
13761
M
 
10981

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1044184
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowT
4th rowT
5th rowT

Common Values

ValueCountFrequency (%)
T 786496
75.3%
E 115859
 
11.1%
2 115316
 
11.0%
V 13761
 
1.3%
M 10981
 
1.1%
C 1771
 
0.2%

Length

2023-06-05T09:52:32.255286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T09:52:32.512549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
t 786496
75.3%
e 115859
 
11.1%
2 115316
 
11.0%
v 13761
 
1.3%
m 10981
 
1.1%
c 1771
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 786496
75.3%
E 115859
 
11.1%
2 115316
 
11.0%
V 13761
 
1.3%
M 10981
 
1.1%
C 1771
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 928868
89.0%
Decimal Number 115316
 
11.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 786496
84.7%
E 115859
 
12.5%
V 13761
 
1.5%
M 10981
 
1.2%
C 1771
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 115316
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 928868
89.0%
Common 115316
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 786496
84.7%
E 115859
 
12.5%
V 13761
 
1.5%
M 10981
 
1.2%
C 1771
 
0.2%
Common
ValueCountFrequency (%)
2 115316
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1044184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 786496
75.3%
E 115859
 
11.1%
2 115316
 
11.0%
V 13761
 
1.3%
M 10981
 
1.1%
C 1771
 
0.2%

BMU Fuel Type
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 MiB
CCGT
464260 
WIND
223190 
OTHER
144282 
PS
72451 
NPSHYD
71964 
Other values (4)
68037 

Length

Max length7
Median length4
Mean length4.242938
Min length2

Characters and Unicode

Total characters4430408
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCCGT
2nd rowCCGT
3rd rowCCGT
4th rowCCGT
5th rowCCGT

Common Values

ValueCountFrequency (%)
CCGT 464260
44.5%
WIND 223190
21.4%
OTHER 144282
 
13.8%
PS 72451
 
6.9%
NPSHYD 71964
 
6.9%
COAL 21600
 
2.1%
BIOMASS 20751
 
2.0%
BATTERY 16037
 
1.5%
OCGT 9649
 
0.9%

Length

2023-06-05T09:52:32.804602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T09:52:33.114730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ccgt 464260
44.5%
wind 223190
21.4%
other 144282
 
13.8%
ps 72451
 
6.9%
npshyd 71964
 
6.9%
coal 21600
 
2.1%
biomass 20751
 
2.0%
battery 16037
 
1.5%
ocgt 9649
 
0.9%

Most occurring characters

ValueCountFrequency (%)
C 959769
21.7%
T 650265
14.7%
G 473909
10.7%
N 295154
 
6.7%
D 295154
 
6.7%
I 243941
 
5.5%
W 223190
 
5.0%
H 216246
 
4.9%
O 196282
 
4.4%
S 185917
 
4.2%
Other values (8) 690581
15.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4430408
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 959769
21.7%
T 650265
14.7%
G 473909
10.7%
N 295154
 
6.7%
D 295154
 
6.7%
I 243941
 
5.5%
W 223190
 
5.0%
H 216246
 
4.9%
O 196282
 
4.4%
S 185917
 
4.2%
Other values (8) 690581
15.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 4430408
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 959769
21.7%
T 650265
14.7%
G 473909
10.7%
N 295154
 
6.7%
D 295154
 
6.7%
I 243941
 
5.5%
W 223190
 
5.0%
H 216246
 
4.9%
O 196282
 
4.4%
S 185917
 
4.2%
Other values (8) 690581
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4430408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 959769
21.7%
T 650265
14.7%
G 473909
10.7%
N 295154
 
6.7%
D 295154
 
6.7%
I 243941
 
5.5%
W 223190
 
5.0%
H 216246
 
4.9%
O 196282
 
4.4%
S 185917
 
4.2%
Other values (8) 690581
15.6%

BMU GSP Group Id
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.8 MiB
_P
273878 
_M
152010 
_D
91581 
_B
86586 
_J
75069 
Other values (9)
365060 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2088368
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row_A
2nd row_J
3rd row_B
4th row_H
5th row_K

Common Values

ValueCountFrequency (%)
_P 273878
26.2%
_M 152010
14.6%
_D 91581
 
8.8%
_B 86586
 
8.3%
_J 75069
 
7.2%
_G 69340
 
6.6%
_N 68166
 
6.5%
_H 56243
 
5.4%
_K 51559
 
4.9%
_L 48596
 
4.7%
Other values (4) 71156
 
6.8%

Length

2023-06-05T09:52:33.379774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p 273878
26.2%
m 152010
14.6%
d 91581
 
8.8%
b 86586
 
8.3%
j 75069
 
7.2%
g 69340
 
6.6%
n 68166
 
6.5%
h 56243
 
5.4%
k 51559
 
4.9%
l 48596
 
4.7%
Other values (4) 71156
 
6.8%

Most occurring characters

ValueCountFrequency (%)
_ 1044184
50.0%
P 273878
 
13.1%
M 152010
 
7.3%
D 91581
 
4.4%
B 86586
 
4.1%
J 75069
 
3.6%
G 69340
 
3.3%
N 68166
 
3.3%
H 56243
 
2.7%
K 51559
 
2.5%
Other values (5) 119752
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Connector Punctuation 1044184
50.0%
Uppercase Letter 1044184
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 273878
26.2%
M 152010
14.6%
D 91581
 
8.8%
B 86586
 
8.3%
J 75069
 
7.2%
G 69340
 
6.6%
N 68166
 
6.5%
H 56243
 
5.4%
K 51559
 
4.9%
L 48596
 
4.7%
Other values (4) 71156
 
6.8%
Connector Punctuation
ValueCountFrequency (%)
_ 1044184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1044184
50.0%
Latin 1044184
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 273878
26.2%
M 152010
14.6%
D 91581
 
8.8%
B 86586
 
8.3%
J 75069
 
7.2%
G 69340
 
6.6%
N 68166
 
6.5%
H 56243
 
5.4%
K 51559
 
4.9%
L 48596
 
4.7%
Other values (4) 71156
 
6.8%
Common
ValueCountFrequency (%)
_ 1044184
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2088368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 1044184
50.0%
P 273878
 
13.1%
M 152010
 
7.3%
D 91581
 
4.4%
B 86586
 
4.1%
J 75069
 
3.6%
G 69340
 
3.3%
N 68166
 
3.3%
H 56243
 
2.7%
K 51559
 
2.5%
Other values (5) 119752
 
5.7%
Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.6 MiB
Northern Scotland
273878 
Yorkshire
152010 
Merseyside and Northern Wales
91581 
East Midlands
86586 
South Eastern England
75069 
Other values (9)
365060 

Length

Max length29
Median length21
Mean length16.872692
Min length6

Characters and Unicode

Total characters17618195
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEastern England
2nd rowSouth Eastern England
3rd rowEast Midlands
4th rowSouthern England
5th rowSouthern Wales

Common Values

ValueCountFrequency (%)
Northern Scotland 273878
26.2%
Yorkshire 152010
14.6%
Merseyside and Northern Wales 91581
 
8.8%
East Midlands 86586
 
8.3%
South Eastern England 75069
 
7.2%
North Western England 69340
 
6.6%
Southern Scotland 68166
 
6.5%
Southern England 56243
 
5.4%
Southern Wales 51559
 
4.9%
South Western England 48596
 
4.7%
Other values (4) 71156
 
6.8%

Length

2023-06-05T09:52:33.622356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
northern 365459
15.8%
scotland 342044
14.8%
england 297137
12.9%
southern 175968
7.6%
yorkshire 152010
6.6%
wales 143140
 
6.2%
south 123665
 
5.4%
eastern 122958
 
5.3%
western 117936
 
5.1%
midlands 100056
 
4.3%
Other values (6) 366829
15.9%

Most occurring characters

ValueCountFrequency (%)
n 1929870
11.0%
r 1617195
 
9.2%
e 1483620
 
8.4%
t 1421900
 
8.1%
1263018
 
7.2%
o 1252554
 
7.1%
a 1183502
 
6.7%
d 1032252
 
5.9%
s 919318
 
5.2%
h 890916
 
5.1%
Other values (14) 4624050
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14139556
80.3%
Uppercase Letter 2215621
 
12.6%
Space Separator 1263018
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1929870
13.6%
r 1617195
11.4%
e 1483620
10.5%
t 1421900
10.1%
o 1252554
8.9%
a 1183502
8.4%
d 1032252
7.3%
s 919318
6.5%
h 890916
6.3%
l 882377
6.2%
Other values (6) 1526052
10.8%
Uppercase Letter
ValueCountFrequency (%)
S 641677
29.0%
E 506681
22.9%
N 439273
19.8%
W 274546
12.4%
M 191637
 
8.6%
Y 152010
 
6.9%
L 9797
 
0.4%
Space Separator
ValueCountFrequency (%)
1263018
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16355177
92.8%
Common 1263018
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1929870
11.8%
r 1617195
9.9%
e 1483620
 
9.1%
t 1421900
 
8.7%
o 1252554
 
7.7%
a 1183502
 
7.2%
d 1032252
 
6.3%
s 919318
 
5.6%
h 890916
 
5.4%
l 882377
 
5.4%
Other values (13) 3741673
22.9%
Common
ValueCountFrequency (%)
1263018
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17618195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1929870
11.0%
r 1617195
 
9.2%
e 1483620
 
8.4%
t 1421900
 
8.1%
1263018
 
7.2%
o 1252554
 
7.1%
a 1183502
 
6.7%
d 1032252
 
5.9%
s 919318
 
5.2%
h 890916
 
5.1%
Other values (14) 4624050
26.2%

LOC LAT
Real number (ℝ)

Distinct234
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.049819
Minimum50.388495
Maximum58.895244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2023-06-05T09:52:33.933273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50.388495
5-th percentile51.054754
Q152.395484
median53.436959
Q356.26201
95-th percentile57.697434
Maximum58.895244
Range8.5067489
Interquartile range (IQR)3.8665258

Descriptive statistics

Standard deviation2.2177816
Coefficient of variation (CV)0.041032175
Kurtosis-0.97417629
Mean54.049819
Median Absolute Deviation (MAD)1.7739817
Skewness0.44192374
Sum56437956
Variance4.9185552
MonotonicityNot monotonic
2023-06-05T09:52:34.226941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.36027054 49378
 
4.7%
51.44354566 40896
 
3.9%
51.68300308 34173
 
3.3%
53.43695862 31776
 
3.0%
53.11937787 30516
 
2.9%
53.23161426 28579
 
2.7%
53.07515403 26922
 
2.6%
53.7351875 26711
 
2.6%
50.89883051 24705
 
2.4%
53.60245366 22670
 
2.2%
Other values (224) 727858
69.7%
ValueCountFrequency (%)
50.38849516 12273
1.2%
50.39684184 44
 
< 0.1%
50.59074232 137
 
< 0.1%
50.62314342 363
 
< 0.1%
50.70742187 13
 
< 0.1%
50.726363 689
 
0.1%
50.74625917 36
 
< 0.1%
50.82951064 8791
 
0.8%
50.89883051 24705
2.4%
50.95594428 378
 
< 0.1%
ValueCountFrequency (%)
58.89524403 6714
0.6%
58.568706 4021
0.4%
58.51031322 6713
0.6%
58.447304 10
 
< 0.1%
58.43441752 4391
0.4%
58.40769912 58
 
< 0.1%
58.35706796 47
 
< 0.1%
58.35051379 913
 
0.1%
58.11242322 3425
0.3%
58.11217999 8061
0.8%

LOC LONG
Real number (ℝ)

Distinct234
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.4029337
Minimum-6.4179968
Maximum2.2416223
Zeros0
Zeros (%)0.0%
Negative948984
Negative (%)90.9%
Memory size8.0 MiB
2023-06-05T09:52:34.512715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6.4179968
5-th percentile-4.9948654
Q1-4.0210241
median-2.670153
Q3-0.81350495
95-th percentile0.69090647
Maximum2.2416223
Range8.6596191
Interquartile range (IQR)3.2075191

Descriptive statistics

Standard deviation1.8466249
Coefficient of variation (CV)-0.76848765
Kurtosis-1.2189913
Mean-2.4029337
Median Absolute Deviation (MAD)1.6753139
Skewness0.14733934
Sum-2509104.9
Variance3.4100235
MonotonicityNot monotonic
2023-06-05T09:52:34.807366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8135049481 49378
 
4.7%
0.7077745465 40896
 
3.9%
-4.994865371 34173
 
3.3%
-2.408211725 31776
 
3.0%
-4.113995377 30516
 
2.9%
-3.081947146 28579
 
2.7%
-0.8561335864 26922
 
2.6%
-0.2432812093 26711
 
2.6%
-1.437187376 24705
 
2.4%
-0.1448286987 22670
 
2.2%
Other values (224) 727858
69.7%
ValueCountFrequency (%)
-6.417996762 4925
0.5%
-5.624398433 5
 
< 0.1%
-5.622701519 280
 
< 0.1%
-5.601716691 137
 
< 0.1%
-5.576595803 3675
0.4%
-5.463966049 36
 
< 0.1%
-5.239121201 113
 
< 0.1%
-5.219652997 20
 
< 0.1%
-5.211471776 2561
0.2%
-5.123928965 1812
 
0.2%
ValueCountFrequency (%)
2.241622324 1406
0.1%
1.91735627 116
 
< 0.1%
1.733725011 2643
0.3%
1.6334 115
 
< 0.1%
1.5927 96
 
< 0.1%
1.395866736 10
 
< 0.1%
1.260268241 141
 
< 0.1%
1.169993728 287
 
< 0.1%
1.1464 10
 
< 0.1%
0.9101120819 2395
0.2%

GSP LOC Center
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.8 MiB
Kingussie
180316 
Leadhills
152825 
Goole
128044 
Milnthorpe
96299 
Pentre-llyn-cymmer
80958 
Other values (9)
405742 

Length

Max length18
Median length15
Mean length9.064684
Min length5

Characters and Unicode

Total characters9465198
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrantham
2nd rowLeadhills
3rd rowMilnthorpe
4th rowLeadhills
5th rowNewmarket

Common Values

ValueCountFrequency (%)
Kingussie 180316
17.3%
Leadhills 152825
14.6%
Goole 128044
12.3%
Milnthorpe 96299
9.2%
Pentre-llyn-cymmer 80958
7.8%
Newmarket 78580
7.5%
Grantham 68704
 
6.6%
Andover 59137
 
5.7%
Bromsgrove 56342
 
5.4%
Brynamman 52381
 
5.0%
Other values (4) 90598
8.7%

Length

2023-06-05T09:52:35.111841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kingussie 180316
17.2%
leadhills 152825
14.6%
goole 128044
12.2%
milnthorpe 96299
9.2%
pentre-llyn-cymmer 80958
7.7%
newmarket 78580
7.5%
grantham 68704
 
6.6%
andover 59137
 
5.6%
bromsgrove 56342
 
5.4%
brynamman 52381
 
5.0%
Other values (5) 94204
9.0%

Most occurring characters

ValueCountFrequency (%)
e 1118057
 
11.8%
l 830695
 
8.8%
n 689298
 
7.3%
r 664354
 
7.0%
i 658422
 
7.0%
o 577025
 
6.1%
s 573405
 
6.1%
m 515364
 
5.4%
a 511834
 
5.4%
t 324541
 
3.4%
Other values (22) 3002203
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8251886
87.2%
Uppercase Letter 1047790
 
11.1%
Dash Punctuation 161916
 
1.7%
Space Separator 3606
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1118057
13.5%
l 830695
10.1%
n 689298
 
8.4%
r 664354
 
8.1%
i 658422
 
8.0%
o 577025
 
7.0%
s 573405
 
6.9%
m 515364
 
6.2%
a 511834
 
6.2%
t 324541
 
3.9%
Other values (11) 1788891
21.7%
Uppercase Letter
ValueCountFrequency (%)
G 196748
18.8%
L 194757
18.6%
K 180316
17.2%
B 112329
10.7%
M 96299
9.2%
P 80958
7.7%
N 78580
 
7.5%
A 62743
 
6.0%
F 45060
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 161916
100.0%
Space Separator
ValueCountFrequency (%)
3606
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9299676
98.3%
Common 165522
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1118057
 
12.0%
l 830695
 
8.9%
n 689298
 
7.4%
r 664354
 
7.1%
i 658422
 
7.1%
o 577025
 
6.2%
s 573405
 
6.2%
m 515364
 
5.5%
a 511834
 
5.5%
t 324541
 
3.5%
Other values (20) 2836681
30.5%
Common
ValueCountFrequency (%)
- 161916
97.8%
3606
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9465198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1118057
 
11.8%
l 830695
 
8.8%
n 689298
 
7.3%
r 664354
 
7.0%
i 658422
 
7.0%
o 577025
 
6.1%
s 573405
 
6.1%
m 515364
 
5.4%
a 511834
 
5.4%
t 324541
 
3.4%
Other values (22) 3002203
31.7%

LOC Center LAT
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.895136
Minimum50.964658
Maximum57.229009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2023-06-05T09:52:35.349842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50.964658
5-th percentile51.054754
Q152.247547
median53.716633
Q355.469264
95-th percentile57.229009
Maximum57.229009
Range6.2643514
Interquartile range (IQR)3.2217173

Descriptive statistics

Standard deviation2.0090187
Coefficient of variation (CV)0.037276438
Kurtosis-1.0023253
Mean53.895136
Median Absolute Deviation (MAD)1.7526309
Skewness0.35541605
Sum56276439
Variance4.0361563
MonotonicityNot monotonic
2023-06-05T09:52:35.562276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
57.22900924 180316
17.3%
55.4692643 152825
14.6%
53.71663341 128044
12.3%
54.26413775 96299
9.2%
53.1695536 80958
7.8%
52.24754696 78580
7.5%
52.91250382 68704
 
6.6%
51.21098811 59137
 
5.7%
52.35337947 56342
 
5.4%
51.81187955 52381
 
5.0%
Other values (4) 90598
8.7%
ValueCountFrequency (%)
50.9646578 34653
 
3.3%
51.05475387 45060
 
4.3%
51.21098811 59137
5.7%
51.51160622 7279
 
0.7%
51.81187955 52381
5.0%
52.24754696 78580
7.5%
52.35337947 56342
5.4%
52.91250382 68704
6.6%
53.1695536 80958
7.8%
53.71663341 128044
12.3%
ValueCountFrequency (%)
57.22900924 180316
17.3%
55.4692643 152825
14.6%
54.70866395 3606
 
0.3%
54.26413775 96299
9.2%
53.71663341 128044
12.3%
53.1695536 80958
7.8%
52.91250382 68704
 
6.6%
52.35337947 56342
 
5.4%
52.24754696 78580
7.5%
51.81187955 52381
 
5.0%

LOC Center LONG
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.4034157
Minimum-4.0788193
Maximum0.44603763
Zeros0
Zeros (%)0.0%
Negative920544
Negative (%)88.2%
Memory size8.0 MiB
2023-06-05T09:52:35.830881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4.0788193
5-th percentile-4.0788193
Q1-3.7678929
median-2.7680731
Q3-0.87357086
95-th percentile0.39405658
Maximum0.44603763
Range4.5248569
Interquartile range (IQR)2.894322

Descriptive statistics

Standard deviation1.5963109
Coefficient of variation (CV)-0.66418428
Kurtosis-1.2622106
Mean-2.4034157
Median Absolute Deviation (MAD)1.2745713
Skewness0.5044734
Sum-2509608.2
Variance2.5482085
MonotonicityNot monotonic
2023-06-05T09:52:36.061816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
-4.078819251 180316
17.3%
-3.736325087 152825
14.6%
-0.8735708584 128044
12.3%
-2.768073086 96299
9.2%
-3.544258467 80958
7.8%
0.3940565826 78580
7.5%
-0.6437712585 68704
 
6.6%
-1.493501823 59137
 
5.7%
-2.062962237 56342
 
5.4%
-3.86639887 52381
 
5.0%
Other values (4) 90598
8.7%
ValueCountFrequency (%)
-4.078819251 180316
17.3%
-3.86639887 52381
 
5.0%
-3.767892866 34653
 
3.3%
-3.736325087 152825
14.6%
-3.544258467 80958
7.8%
-2.768073086 96299
9.2%
-2.062962237 56342
 
5.4%
-1.671818006 3606
 
0.3%
-1.493501823 59137
 
5.7%
-0.8735708584 128044
12.3%
ValueCountFrequency (%)
0.4460376274 45060
 
4.3%
0.3940565826 78580
7.5%
-0.127985862 7279
 
0.7%
-0.6437712585 68704
6.6%
-0.8735708584 128044
12.3%
-1.493501823 59137
5.7%
-1.671818006 3606
 
0.3%
-2.062962237 56342
5.4%
-2.768073086 96299
9.2%
-3.544258467 80958
7.8%

BMU Party ID
Categorical

Distinct125
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.5 MiB
Uniper UK Limited
116884 
SSE Generation Ltd
98415 
RWE Generation UK plc
93970 
Statkraft Markets Gmbh
 
51806
West Burton B Limited
 
48479
Other values (120)
634630 

Length

Max length31
Median length28
Mean length20.823359
Min length4

Characters and Unicode

Total characters21743418
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRWE Generation UK plc
2nd rowVPI Power Limited
3rd rowUniper UK Limited
4th rowMarchwood Power Limited
5th rowRWE Generation UK plc

Common Values

ValueCountFrequency (%)
Uniper UK Limited 116884
 
11.2%
SSE Generation Ltd 98415
 
9.4%
RWE Generation UK plc 93970
 
9.0%
Statkraft Markets Gmbh 51806
 
5.0%
West Burton B Limited 48479
 
4.6%
First Hydro Company 38547
 
3.7%
Conrad Energy (Trading) 35598
 
3.4%
EP UK INVESTMENTS LIMITED 34943
 
3.3%
Carrington Power Ltd 31776
 
3.0%
SCCL 26711
 
2.6%
Other values (115) 467055
44.7%

Length

2023-06-05T09:52:36.330646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
limited 481603
 
14.0%
uk 290222
 
8.4%
ltd 264958
 
7.7%
generation 211127
 
6.1%
power 177516
 
5.2%
energy 119960
 
3.5%
uniper 116884
 
3.4%
sse 112886
 
3.3%
rwe 94149
 
2.7%
plc 94028
 
2.7%
Other values (181) 1471722
42.8%

Most occurring characters

ValueCountFrequency (%)
2390871
 
11.0%
e 1942067
 
8.9%
i 1622515
 
7.5%
t 1492615
 
6.9%
r 1413316
 
6.5%
n 1215191
 
5.6%
a 1024748
 
4.7%
d 1023380
 
4.7%
o 844369
 
3.9%
L 819692
 
3.8%
Other values (46) 7954654
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14229362
65.4%
Uppercase Letter 4967900
 
22.8%
Space Separator 2390871
 
11.0%
Close Punctuation 70236
 
0.3%
Open Punctuation 70236
 
0.3%
Other Punctuation 10360
 
< 0.1%
Decimal Number 4453
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1942067
13.6%
i 1622515
11.4%
t 1492615
10.5%
r 1413316
9.9%
n 1215191
8.5%
a 1024748
7.2%
d 1023380
7.2%
o 844369
 
5.9%
m 763133
 
5.4%
p 340170
 
2.4%
Other values (14) 2547858
17.9%
Uppercase Letter
ValueCountFrequency (%)
L 819692
16.5%
E 518185
10.4%
S 499012
10.0%
U 409552
 
8.2%
P 301906
 
6.1%
K 299663
 
6.0%
G 288787
 
5.8%
W 271882
 
5.5%
C 243367
 
4.9%
M 183401
 
3.7%
Other values (14) 1132453
22.8%
Decimal Number
ValueCountFrequency (%)
2 3141
70.5%
1 1072
 
24.1%
3 240
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 9005
86.9%
& 1355
 
13.1%
Space Separator
ValueCountFrequency (%)
2390871
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70236
100.0%
Open Punctuation
ValueCountFrequency (%)
( 70236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19197262
88.3%
Common 2546156
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1942067
 
10.1%
i 1622515
 
8.5%
t 1492615
 
7.8%
r 1413316
 
7.4%
n 1215191
 
6.3%
a 1024748
 
5.3%
d 1023380
 
5.3%
o 844369
 
4.4%
L 819692
 
4.3%
m 763133
 
4.0%
Other values (38) 7036236
36.7%
Common
ValueCountFrequency (%)
2390871
93.9%
) 70236
 
2.8%
( 70236
 
2.8%
. 9005
 
0.4%
2 3141
 
0.1%
& 1355
 
0.1%
1 1072
 
< 0.1%
3 240
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21743418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2390871
 
11.0%
e 1942067
 
8.9%
i 1622515
 
7.5%
t 1492615
 
6.9%
r 1413316
 
6.5%
n 1215191
 
5.6%
a 1024748
 
4.7%
d 1023380
 
4.7%
o 844369
 
3.9%
L 819692
 
3.8%
Other values (46) 7954654
36.6%

BMU Party Name
Categorical

Distinct125
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 MiB
EECL
116884 
SSEGEN
98415 
INNOGY01
93970 
STATKRA1
 
51806
WESTBURB
 
48479
Other values (120)
634630 

Length

Max length8
Median length7
Mean length6.2028991
Min length2

Characters and Unicode

Total characters6476968
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINNOGY01
2nd rowSPGEN01
3rd rowEECL
4th rowMPL
5th rowINNOGY01

Common Values

ValueCountFrequency (%)
EECL 116884
 
11.2%
SSEGEN 98415
 
9.4%
INNOGY01 93970
 
9.0%
STATKRA1 51806
 
5.0%
WESTBURB 48479
 
4.6%
FSTHYDRO 38547
 
3.7%
CONRAD 35598
 
3.4%
EPUKI 34943
 
3.3%
CARRINGT 31776
 
3.0%
SALTEND 26711
 
2.6%
Other values (115) 467055
44.7%

Length

2023-06-05T09:52:36.612327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
eecl 116884
 
11.2%
ssegen 98415
 
9.4%
innogy01 93970
 
9.0%
statkra1 51806
 
5.0%
westburb 48479
 
4.6%
fsthydro 38547
 
3.7%
conrad 35598
 
3.4%
epuki 34943
 
3.3%
carringt 31776
 
3.0%
saltend 26711
 
2.6%
Other values (115) 467055
44.7%

Most occurring characters

ValueCountFrequency (%)
E 777534
 
12.0%
S 573174
 
8.8%
N 522282
 
8.1%
R 427294
 
6.6%
L 405756
 
6.3%
A 371416
 
5.7%
T 342458
 
5.3%
C 320965
 
5.0%
G 317874
 
4.9%
O 260784
 
4.0%
Other values (24) 2157431
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6015861
92.9%
Decimal Number 461107
 
7.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 777534
12.9%
S 573174
 
9.5%
N 522282
 
8.7%
R 427294
 
7.1%
L 405756
 
6.7%
A 371416
 
6.2%
T 342458
 
5.7%
C 320965
 
5.3%
G 317874
 
5.3%
O 260784
 
4.3%
Other values (15) 1696324
28.2%
Decimal Number
ValueCountFrequency (%)
1 223739
48.5%
0 206924
44.9%
2 17251
 
3.7%
6 6112
 
1.3%
3 4597
 
1.0%
7 1898
 
0.4%
5 241
 
0.1%
9 223
 
< 0.1%
8 122
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 6015861
92.9%
Common 461107
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 777534
12.9%
S 573174
 
9.5%
N 522282
 
8.7%
R 427294
 
7.1%
L 405756
 
6.7%
A 371416
 
6.2%
T 342458
 
5.7%
C 320965
 
5.3%
G 317874
 
5.3%
O 260784
 
4.3%
Other values (15) 1696324
28.2%
Common
ValueCountFrequency (%)
1 223739
48.5%
0 206924
44.9%
2 17251
 
3.7%
6 6112
 
1.3%
3 4597
 
1.0%
7 1898
 
0.4%
5 241
 
0.1%
9 223
 
< 0.1%
8 122
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6476968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 777534
 
12.0%
S 573174
 
8.8%
N 522282
 
8.1%
R 427294
 
6.6%
L 405756
 
6.3%
A 371416
 
5.7%
T 342458
 
5.3%
C 320965
 
5.0%
G 317874
 
4.9%
O 260784
 
4.0%
Other values (24) 2157431
33.3%

Trading Unit
Categorical

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)< 0.1%
Missing416890
Missing (%)39.9%
Memory size57.2 MiB
West Burton A&B Power Stations
49378 
DEFAULT__P
46654 
GRAIN PS TRADING UNIT
 
41090
Pembroke Power Station
 
34173
DEFAULT__G
 
32320
Other values (33)
423679 

Length

Max length30
Median length25
Mean length17.312255
Min length8

Characters and Unicode

Total characters10859874
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPembroke Power Station
2nd rowSALTEND1
3rd rowSOUTH HUMBER BANK P/S.
4th rowGRAIN PS TRADING UNIT
5th rowGRAIN PS TRADING UNIT

Common Values

ValueCountFrequency (%)
West Burton A&B Power Stations 49378
 
4.7%
DEFAULT__P 46654
 
4.5%
GRAIN PS TRADING UNIT 41090
 
3.9%
Pembroke Power Station 34173
 
3.3%
DEFAULT__G 32320
 
3.1%
DINORWIG 30516
 
2.9%
CONNAHS QUAY PS TRADING UNIT 28579
 
2.7%
Staythorpe Power Station 26922
 
2.6%
SALTEND1 26711
 
2.6%
SOUTH HUMBER BANK P/S. 22670
 
2.2%
Other values (28) 288281
27.6%
(Missing) 416890
39.9%

Length

2023-06-05T09:52:36.938876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
power 211256
 
13.3%
station 161878
 
10.2%
trading 110545
 
6.9%
unit 110545
 
6.9%
ps 91563
 
5.8%
west 49378
 
3.1%
a&b 49378
 
3.1%
stations 49378
 
3.1%
burton 49378
 
3.1%
default__p 46654
 
2.9%
Other values (46) 661553
41.6%

Most occurring characters

ValueCountFrequency (%)
964212
 
8.9%
t 634806
 
5.8%
o 550368
 
5.1%
A 530047
 
4.9%
T 479922
 
4.4%
S 467505
 
4.3%
_ 432142
 
4.0%
P 416266
 
3.8%
a 402236
 
3.7%
U 400535
 
3.7%
Other values (42) 5581835
51.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5332584
49.1%
Lowercase Letter 4009507
36.9%
Space Separator 964212
 
8.9%
Connector Punctuation 432142
 
4.0%
Other Punctuation 94718
 
0.9%
Decimal Number 26711
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 634806
15.8%
o 550368
13.7%
a 402236
10.0%
e 400220
10.0%
r 390898
9.7%
n 376783
9.4%
i 328256
8.2%
w 194977
 
4.9%
s 118115
 
2.9%
u 66742
 
1.7%
Other values (13) 546106
13.6%
Uppercase Letter
ValueCountFrequency (%)
A 530047
 
9.9%
T 479922
 
9.0%
S 467505
 
8.8%
P 416266
 
7.8%
U 400535
 
7.5%
D 392799
 
7.4%
N 364739
 
6.8%
E 312540
 
5.9%
I 280782
 
5.3%
L 268283
 
5.0%
Other values (13) 1419166
26.6%
Other Punctuation
ValueCountFrequency (%)
& 49378
52.1%
. 22670
23.9%
/ 22670
23.9%
Space Separator
ValueCountFrequency (%)
964212
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 432142
100.0%
Decimal Number
ValueCountFrequency (%)
1 26711
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9342091
86.0%
Common 1517783
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 634806
 
6.8%
o 550368
 
5.9%
A 530047
 
5.7%
T 479922
 
5.1%
S 467505
 
5.0%
P 416266
 
4.5%
a 402236
 
4.3%
U 400535
 
4.3%
e 400220
 
4.3%
D 392799
 
4.2%
Other values (36) 4667387
50.0%
Common
ValueCountFrequency (%)
964212
63.5%
_ 432142
28.5%
& 49378
 
3.3%
1 26711
 
1.8%
. 22670
 
1.5%
/ 22670
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10859874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
964212
 
8.9%
t 634806
 
5.8%
o 550368
 
5.1%
A 530047
 
4.9%
T 479922
 
4.4%
S 467505
 
4.3%
_ 432142
 
4.0%
P 416266
 
3.8%
a 402236
 
3.7%
U 400535
 
3.7%
Other values (42) 5581835
51.4%

PC Flag
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing907900
Missing (%)86.9%
Memory size37.0 MiB
Consumption (C)
82542 
Production (P)
53742 

Length

Max length15
Median length15
Mean length14.605662
Min length14

Characters and Unicode

Total characters1990518
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumption (C)
2nd rowConsumption (C)
3rd rowConsumption (C)
4th rowConsumption (C)
5th rowConsumption (C)

Common Values

ValueCountFrequency (%)
Consumption (C) 82542
 
7.9%
Production (P) 53742
 
5.1%
(Missing) 907900
86.9%

Length

2023-06-05T09:52:37.181682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T09:52:37.423572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
consumption 82542
30.3%
c 82542
30.3%
production 53742
19.7%
p 53742
19.7%

Most occurring characters

ValueCountFrequency (%)
o 272568
13.7%
n 218826
11.0%
C 165084
8.3%
u 136284
 
6.8%
t 136284
 
6.8%
i 136284
 
6.8%
136284
 
6.8%
( 136284
 
6.8%
) 136284
 
6.8%
P 107484
 
5.4%
Other values (6) 408852
20.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1309098
65.8%
Uppercase Letter 272568
 
13.7%
Space Separator 136284
 
6.8%
Open Punctuation 136284
 
6.8%
Close Punctuation 136284
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 272568
20.8%
n 218826
16.7%
u 136284
10.4%
t 136284
10.4%
i 136284
10.4%
s 82542
 
6.3%
m 82542
 
6.3%
p 82542
 
6.3%
r 53742
 
4.1%
d 53742
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
C 165084
60.6%
P 107484
39.4%
Space Separator
ValueCountFrequency (%)
136284
100.0%
Open Punctuation
ValueCountFrequency (%)
( 136284
100.0%
Close Punctuation
ValueCountFrequency (%)
) 136284
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1581666
79.5%
Common 408852
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 272568
17.2%
n 218826
13.8%
C 165084
10.4%
u 136284
8.6%
t 136284
8.6%
i 136284
8.6%
P 107484
 
6.8%
s 82542
 
5.2%
m 82542
 
5.2%
p 82542
 
5.2%
Other values (3) 161226
10.2%
Common
ValueCountFrequency (%)
136284
33.3%
( 136284
33.3%
) 136284
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1990518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 272568
13.7%
n 218826
11.0%
C 165084
8.3%
u 136284
 
6.8%
t 136284
 
6.8%
i 136284
 
6.8%
136284
 
6.8%
( 136284
 
6.8%
) 136284
 
6.8%
P 107484
 
5.4%
Other values (6) 408852
20.5%

PC Status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.9 MiB
Production (P)
828612 
Consumption (C)
215572 

Length

Max length15
Median length14
Mean length14.20645
Min length14

Characters and Unicode

Total characters14834148
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProduction (P)
2nd rowProduction (P)
3rd rowProduction (P)
4th rowProduction (P)
5th rowProduction (P)

Common Values

ValueCountFrequency (%)
Production (P) 828612
79.4%
Consumption (C) 215572
 
20.6%

Length

2023-06-05T09:52:37.758533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T09:52:38.239377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
production 828612
39.7%
p 828612
39.7%
consumption 215572
 
10.3%
c 215572
 
10.3%

Most occurring characters

ValueCountFrequency (%)
o 2088368
14.1%
P 1657224
11.2%
n 1259756
8.5%
u 1044184
 
7.0%
t 1044184
 
7.0%
i 1044184
 
7.0%
1044184
 
7.0%
( 1044184
 
7.0%
) 1044184
 
7.0%
r 828612
 
5.6%
Other values (6) 2735084
18.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9613228
64.8%
Uppercase Letter 2088368
 
14.1%
Space Separator 1044184
 
7.0%
Open Punctuation 1044184
 
7.0%
Close Punctuation 1044184
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2088368
21.7%
n 1259756
13.1%
u 1044184
10.9%
t 1044184
10.9%
i 1044184
10.9%
r 828612
 
8.6%
d 828612
 
8.6%
c 828612
 
8.6%
s 215572
 
2.2%
m 215572
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
P 1657224
79.4%
C 431144
 
20.6%
Space Separator
ValueCountFrequency (%)
1044184
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1044184
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1044184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11701596
78.9%
Common 3132552
 
21.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2088368
17.8%
P 1657224
14.2%
n 1259756
10.8%
u 1044184
8.9%
t 1044184
8.9%
i 1044184
8.9%
r 828612
 
7.1%
d 828612
 
7.1%
c 828612
 
7.1%
C 431144
 
3.7%
Other values (3) 646716
 
5.5%
Common
ValueCountFrequency (%)
1044184
33.3%
( 1044184
33.3%
) 1044184
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14834148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2088368
14.1%
P 1657224
11.2%
n 1259756
8.5%
u 1044184
 
7.0%
t 1044184
 
7.0%
i 1044184
 
7.0%
1044184
 
7.0%
( 1044184
 
7.0%
) 1044184
 
7.0%
r 828612
 
5.6%
Other values (6) 2735084
18.4%

Transmission Loss Factor
Real number (ℝ)

Distinct151
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0045813018
Minimum-0.0522058
Maximum0.0315465
Zeros0
Zeros (%)0.0%
Negative486566
Negative (%)46.6%
Memory size8.0 MiB
2023-06-05T09:52:38.705690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.0522058
5-th percentile-0.0522058
Q1-0.0188698
median0.0037283
Q30.0119312
95-th percentile0.0314595
Maximum0.0315465
Range0.0837523
Interquartile range (IQR)0.030801

Descriptive statistics

Standard deviation0.025210036
Coefficient of variation (CV)-5.5028105
Kurtosis-0.85592377
Mean-0.0045813018
Median Absolute Deviation (MAD)0.0145163
Skewness-0.55078214
Sum-4783.722
Variance0.0006355459
MonotonicityNot monotonic
2023-06-05T09:52:39.223068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0398132 127521
 
12.2%
-0.0522058 88914
 
8.5%
0.0118813 65854
 
6.3%
0.0044415 56372
 
5.4%
0.0037283 55650
 
5.3%
0.0100104 55223
 
5.3%
-0.0060379 50385
 
4.8%
-0.0090952 43753
 
4.2%
-0.010788 43384
 
4.2%
0.0229594 36681
 
3.5%
Other values (141) 420447
40.3%
ValueCountFrequency (%)
-0.0522058 88914
8.5%
-0.0399698 49
 
< 0.1%
-0.0398132 127521
12.2%
-0.0381051 23094
 
2.2%
-0.0374553 409
 
< 0.1%
-0.037195 69
 
< 0.1%
-0.0351173 4838
 
0.5%
-0.029104 767
 
0.1%
-0.0261238 1783
 
0.2%
-0.0242162 328
 
< 0.1%
ValueCountFrequency (%)
0.0315465 24734
2.4%
0.0314595 29226
2.8%
0.0309432 2356
 
0.2%
0.0298018 20210
1.9%
0.0250176 21510
2.1%
0.0241869 26279
2.5%
0.0238107 5982
 
0.6%
0.0237533 228
 
< 0.1%
0.0229594 36681
3.5%
0.0229315 7981
 
0.8%

Generation Capacity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct298
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean333.10854
Minimum0
Maximum1283
Zeros23453
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2023-06-05T09:52:39.919717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q149
median320
Q3470
95-th percentile920
Maximum1283
Range1283
Interquartile range (IQR)421

Descriptive statistics

Standard deviation306.60313
Coefficient of variation (CV)0.9204301
Kurtosis0.024061289
Mean333.10854
Median Absolute Deviation (MAD)251
Skewness0.84339077
Sum3.4782661 × 108
Variance94005.477
MonotonicityNot monotonic
2023-06-05T09:52:40.520780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
470 80430
 
7.7%
20 46044
 
4.4%
468 40896
 
3.9%
475 34173
 
3.3%
50 31718
 
3.0%
320 30516
 
2.9%
464 26922
 
2.6%
920 24705
 
2.4%
810 24478
 
2.3%
0 23453
 
2.2%
Other values (288) 680849
65.2%
ValueCountFrequency (%)
0 23453
2.2%
2 11
 
< 0.1%
4 396
 
< 0.1%
4.7 6
 
< 0.1%
5 945
 
0.1%
5.88 1909
 
0.2%
6 4590
 
0.4%
6.121 145
 
< 0.1%
7.12 686
 
0.1%
7.125 3
 
< 0.1%
ValueCountFrequency (%)
1283 9187
 
0.9%
1206.288 26
 
< 0.1%
1206.28 28
 
< 0.1%
1200 12164
1.2%
950 11462
1.1%
920 24705
2.4%
905 12273
1.2%
870.616 329
 
< 0.1%
848.891 27
 
< 0.1%
830 13808
1.3%

Demand Capacity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct172
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.881558
Minimum-366.184
Maximum0
Zeros206022
Zeros (%)19.7%
Negative838162
Negative (%)80.3%
Memory size8.0 MiB
2023-06-05T09:52:41.141964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-366.184
5-th percentile-134
Q1-15
median-10
Q3-1
95-th percentile0
Maximum0
Range366.184
Interquartile range (IQR)14

Descriptive statistics

Standard deviation54.985382
Coefficient of variation (CV)-2.4030436
Kurtosis16.136517
Mean-22.881558
Median Absolute Deviation (MAD)8.9
Skewness-4.0004655
Sum-23892557
Variance3023.3922
MonotonicityNot monotonic
2023-06-05T09:52:41.679117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 206022
19.7%
-10 109277
 
10.5%
-1 72661
 
7.0%
-2 61887
 
5.9%
-11 56862
 
5.4%
-15 56015
 
5.4%
-14 45976
 
4.4%
-20 41854
 
4.0%
-12 34108
 
3.3%
-60 29966
 
2.9%
Other values (162) 329556
31.6%
ValueCountFrequency (%)
-366.184 268
 
< 0.1%
-302.2 4807
 
0.5%
-299 7127
 
0.7%
-294 18582
1.8%
-156 16540
1.6%
-134 13656
1.3%
-120 3708
 
0.4%
-100 24
 
< 0.1%
-75 8031
0.8%
-70 138
 
< 0.1%
ValueCountFrequency (%)
0 206022
19.7%
-0.074 716
 
0.1%
-0.08 102
 
< 0.1%
-0.088 139
 
< 0.1%
-0.1 10106
 
1.0%
-0.12 20
 
< 0.1%
-0.14 316
 
< 0.1%
-0.16 705
 
0.1%
-0.2 8928
 
0.9%
-0.26 391
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.6 MiB
False (F)
921542 
True (T)
122642 

Length

Max length9
Median length9
Mean length8.8825475
Min length8

Characters and Unicode

Total characters9275014
Distinct characters11
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse (F)
2nd rowFalse (F)
3rd rowFalse (F)
4th rowFalse (F)
5th rowFalse (F)

Common Values

ValueCountFrequency (%)
False (F) 921542
88.3%
True (T) 122642
 
11.7%

Length

2023-06-05T09:52:42.984681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T09:52:44.166377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
false 921542
44.1%
f 921542
44.1%
true 122642
 
5.9%
t 122642
 
5.9%

Most occurring characters

ValueCountFrequency (%)
F 1843084
19.9%
e 1044184
11.3%
1044184
11.3%
( 1044184
11.3%
) 1044184
11.3%
a 921542
9.9%
l 921542
9.9%
s 921542
9.9%
T 245284
 
2.6%
r 122642
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4054094
43.7%
Uppercase Letter 2088368
22.5%
Space Separator 1044184
 
11.3%
Open Punctuation 1044184
 
11.3%
Close Punctuation 1044184
 
11.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1044184
25.8%
a 921542
22.7%
l 921542
22.7%
s 921542
22.7%
r 122642
 
3.0%
u 122642
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
F 1843084
88.3%
T 245284
 
11.7%
Space Separator
ValueCountFrequency (%)
1044184
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1044184
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1044184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6142462
66.2%
Common 3132552
33.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 1843084
30.0%
e 1044184
17.0%
a 921542
15.0%
l 921542
15.0%
s 921542
15.0%
T 245284
 
4.0%
r 122642
 
2.0%
u 122642
 
2.0%
Common
ValueCountFrequency (%)
1044184
33.3%
( 1044184
33.3%
) 1044184
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9275014
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 1843084
19.9%
e 1044184
11.3%
1044184
11.3%
( 1044184
11.3%
) 1044184
11.3%
a 921542
9.9%
l 921542
9.9%
s 921542
9.9%
T 245284
 
2.6%
r 122642
 
1.3%

Base TU Flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.5 MiB
False (F)
828113 
True (T)
216071 

Length

Max length9
Median length9
Mean length8.7930719
Min length8

Characters and Unicode

Total characters9181585
Distinct characters11
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse (F)
2nd rowFalse (F)
3rd rowFalse (F)
4th rowFalse (F)
5th rowFalse (F)

Common Values

ValueCountFrequency (%)
False (F) 828113
79.3%
True (T) 216071
 
20.7%

Length

2023-06-05T09:52:44.760272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T09:52:45.199509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
false 828113
39.7%
f 828113
39.7%
true 216071
 
10.3%
t 216071
 
10.3%

Most occurring characters

ValueCountFrequency (%)
F 1656226
18.0%
e 1044184
11.4%
1044184
11.4%
( 1044184
11.4%
) 1044184
11.4%
a 828113
9.0%
l 828113
9.0%
s 828113
9.0%
T 432142
 
4.7%
r 216071
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3960665
43.1%
Uppercase Letter 2088368
22.7%
Space Separator 1044184
 
11.4%
Open Punctuation 1044184
 
11.4%
Close Punctuation 1044184
 
11.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1044184
26.4%
a 828113
20.9%
l 828113
20.9%
s 828113
20.9%
r 216071
 
5.5%
u 216071
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
F 1656226
79.3%
T 432142
 
20.7%
Space Separator
ValueCountFrequency (%)
1044184
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1044184
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1044184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6049033
65.9%
Common 3132552
34.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 1656226
27.4%
e 1044184
17.3%
a 828113
13.7%
l 828113
13.7%
s 828113
13.7%
T 432142
 
7.1%
r 216071
 
3.6%
u 216071
 
3.6%
Common
ValueCountFrequency (%)
1044184
33.3%
( 1044184
33.3%
) 1044184
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9181585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 1656226
18.0%
e 1044184
11.4%
1044184
11.4%
( 1044184
11.4%
) 1044184
11.4%
a 828113
9.0%
l 828113
9.0%
s 828113
9.0%
T 432142
 
4.7%
r 216071
 
2.4%

FPN Flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.7 MiB
True (T)
1038316 
False (F)
 
5868

Length

Max length9
Median length8
Mean length8.0056197
Min length8

Characters and Unicode

Total characters8359340
Distinct characters11
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue (T)
2nd rowTrue (T)
3rd rowTrue (T)
4th rowTrue (T)
5th rowTrue (T)

Common Values

ValueCountFrequency (%)
True (T) 1038316
99.4%
False (F) 5868
 
0.6%

Length

2023-06-05T09:52:45.673539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T09:52:45.979084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
true 1038316
49.7%
t 1038316
49.7%
false 5868
 
0.3%
f 5868
 
0.3%

Most occurring characters

ValueCountFrequency (%)
T 2076632
24.8%
e 1044184
12.5%
1044184
12.5%
( 1044184
12.5%
) 1044184
12.5%
r 1038316
12.4%
u 1038316
12.4%
F 11736
 
0.1%
a 5868
 
0.1%
l 5868
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3138420
37.5%
Uppercase Letter 2088368
25.0%
Space Separator 1044184
 
12.5%
Open Punctuation 1044184
 
12.5%
Close Punctuation 1044184
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1044184
33.3%
r 1038316
33.1%
u 1038316
33.1%
a 5868
 
0.2%
l 5868
 
0.2%
s 5868
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
T 2076632
99.4%
F 11736
 
0.6%
Space Separator
ValueCountFrequency (%)
1044184
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1044184
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1044184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5226788
62.5%
Common 3132552
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 2076632
39.7%
e 1044184
20.0%
r 1038316
19.9%
u 1038316
19.9%
F 11736
 
0.2%
a 5868
 
0.1%
l 5868
 
0.1%
s 5868
 
0.1%
Common
ValueCountFrequency (%)
1044184
33.3%
( 1044184
33.3%
) 1044184
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8359340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 2076632
24.8%
e 1044184
12.5%
1044184
12.5%
( 1044184
12.5%
) 1044184
12.5%
r 1038316
12.4%
u 1038316
12.4%
F 11736
 
0.1%
a 5868
 
0.1%
l 5868
 
0.1%

Interactions

2023-06-05T09:52:00.682197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:05.415581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:11.399122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:16.008598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:20.306414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:26.319432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:30.838214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:35.211277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:40.942386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:45.966798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:50.365107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:55.772456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:52:01.062197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:05.800422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:11.749505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:16.378833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:20.663709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:26.686202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:31.200004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:35.567498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:41.444949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:46.337427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:50.711566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:56.327987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:52:01.436778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:06.306244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:12.350107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:16.740511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:21.022109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:27.048223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:31.567936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:35.934509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:41.925676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:46.715237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:51.072988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:56.818336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:52:01.817793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:06.772975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:12.692857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:17.087221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:21.417422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:27.639174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:31.932712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:36.291546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:42.306443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:47.084644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:51.418862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:57.324835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:52:02.186358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:07.329630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:13.082024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:17.446364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:21.923925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:27.995651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:32.299006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:36.719687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:42.677426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:47.446700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:51.787961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:57.690718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:52:02.540270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:07.835859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:13.439781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:17.788758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:22.472541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:28.344464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:32.639604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:37.245002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:43.035422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:47.811241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:52.252663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:58.043486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:52:02.907294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:08.339962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:13.790792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:18.133095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:22.998244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:28.693739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:33.004658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:37.727018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:43.419648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:48.171663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:52.753648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:58.397450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:52:03.263211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:08.855671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:14.168617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:18.499364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:23.509363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:29.024628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:33.358067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:38.280060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:43.761729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:48.533318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:53.223364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:58.798906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:52:03.629898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:09.324741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:14.545037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:18.863077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:24.058470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:29.384674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:33.729669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:38.793669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:44.137192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:48.911258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:53.760485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:59.173608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:52:04.007081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:09.863552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:14.906694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:19.211028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:24.601622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:29.736542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:34.097895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:39.339869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:44.513001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:49.260475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:54.229646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:59.526774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:52:04.370070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:10.395516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:15.268315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:19.556444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:25.150825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:30.084727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:34.443003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:39.855166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:44.881337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:49.622109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:54.727634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:59.918337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:52:04.740546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:10.905652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:15.636773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:19.931788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:25.726375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:30.448062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:34.835817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:40.397943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:45.270075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:49.987685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:51:55.220445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-05T09:52:00.315492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-05T09:52:46.208294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
settlementdate_monthsettlementdate_daysettlementperiodacceptedpriceacceptedvolumeLOC LATLOC LONGLOC Center LATLOC Center LONGTransmission Loss FactorGeneration CapacityDemand Capacityrecordtypesettlementdate_yearBMU TypeBMU Fuel TypeBMU GSP Group IdBMU GSP Group NameGSP LOC CenterTrading UnitPC FlagPC StatusExempt Export FlagBase TU FlagFPN Flag
settlementdate_month1.0000.0170.0100.125-0.0180.007-0.0160.014-0.0100.006-0.0100.0140.1280.3690.0690.1020.0840.0840.0360.0970.1370.1140.0460.1060.079
settlementdate_day0.0171.000-0.0120.004-0.011-0.0070.006-0.0050.0010.0040.013-0.0060.0310.0560.0170.0310.0250.0250.0150.0360.0550.0280.0220.0250.015
settlementperiod0.010-0.0121.0000.085-0.007-0.0350.011-0.0010.0090.035-0.0370.0070.0790.0220.0820.1020.0550.0550.0230.1070.1440.1700.0500.1630.050
acceptedprice0.1250.0040.0851.0000.553-0.4670.3280.032-0.0180.4950.152-0.1940.0520.0170.0110.0530.0310.0310.0230.1880.0160.0180.0110.0180.003
acceptedvolume-0.018-0.011-0.0070.5531.000-0.2590.1390.0150.0520.277-0.065-0.0290.5710.0470.1660.1760.1330.1330.1260.2620.0120.3340.2450.3600.055
LOC LAT0.007-0.007-0.035-0.467-0.2591.000-0.505-0.045-0.002-0.841-0.4310.3930.3190.1010.2330.4270.5240.5240.3070.6450.5310.2910.2840.2920.195
LOC LONG-0.0160.0060.0110.3280.139-0.5051.0000.072-0.0440.4680.407-0.1740.1950.0640.1730.3850.5450.5450.2990.8080.4610.1470.3130.1780.104
LOC Center LAT0.014-0.005-0.0010.0320.015-0.0450.0721.000-0.6310.1340.155-0.2440.1300.0530.1040.2510.3020.3021.0000.5800.4370.2180.1690.1520.101
LOC Center LONG-0.0100.0010.009-0.0180.052-0.002-0.044-0.6311.000-0.044-0.2150.1940.0650.0380.1450.2130.2900.2901.0000.5430.3390.1920.1660.1570.087
Transmission Loss Factor0.0060.0040.0350.4950.277-0.8410.4680.134-0.0441.0000.378-0.3230.3840.4090.1570.3900.6900.6900.2300.7600.4360.2370.2200.1730.104
Generation Capacity-0.0100.013-0.0370.152-0.065-0.4310.4070.155-0.2150.3781.000-0.5600.1730.0810.2820.4540.4470.4470.3290.9190.0190.6220.4440.6230.092
Demand Capacity0.014-0.0060.007-0.194-0.0290.393-0.174-0.2440.194-0.323-0.5601.0000.1310.0450.0760.4260.3420.3420.2990.8690.0590.2790.1110.1430.026
recordtype0.1280.0310.0790.0520.5710.3190.1950.1300.0650.3840.1730.1311.0000.1250.2790.5150.4240.4240.1400.4720.3080.2620.0130.2220.084
settlementdate_year0.3690.0560.0220.0170.0470.1010.0640.0530.0380.4090.0810.0450.1251.0000.0480.1080.1110.1110.0700.1560.1730.0690.0230.0540.082
BMU Type0.0690.0170.0820.0110.1660.2330.1730.1040.1450.1570.2820.0760.2790.0481.0000.4390.2330.2330.2010.6490.2190.8100.8090.9570.213
BMU Fuel Type0.1020.0310.1020.0530.1760.4270.3850.2510.2130.3900.4540.4260.5150.1080.4391.0000.4530.4530.2990.7970.4890.7980.3930.7860.187
BMU GSP Group Id0.0840.0250.0550.0310.1330.5240.5450.3020.2900.6900.4470.3420.4240.1110.2330.4531.0001.0000.2690.9960.4880.3650.2700.3760.103
BMU GSP Group Name0.0840.0250.0550.0310.1330.5240.5450.3020.2900.6900.4470.3420.4240.1110.2330.4531.0001.0000.2690.9960.4880.3650.2700.3760.103
GSP LOC Center0.0360.0150.0230.0230.1260.3070.2991.0001.0000.2300.3290.2990.1400.0700.2010.2990.2690.2691.0000.5400.4730.2280.2540.1820.120
Trading Unit0.0970.0360.1070.1880.2620.6450.8080.5800.5430.7600.9190.8690.4720.1560.6490.7970.9960.9960.5401.0000.6030.9270.7221.0000.321
PC Flag0.1370.0550.1440.0160.0120.5310.4610.4370.3390.4360.0190.0590.3080.1730.2190.4890.4880.4880.4730.6031.0001.0000.1550.1961.000
PC Status0.1140.0280.1700.0180.3340.2910.1470.2180.1920.2370.6220.2790.2620.0690.8100.7980.3650.3650.2280.9271.0001.0000.3830.8020.147
Exempt Export Flag0.0460.0220.0500.0110.2450.2840.3130.1690.1660.2200.4440.1110.0130.0230.8090.3930.2700.2700.2540.7220.1550.3831.0000.5550.027
Base TU Flag0.1060.0250.1630.0180.3600.2920.1780.1520.1570.1730.6230.1430.2220.0540.9570.7860.3760.3760.1821.0000.1960.8020.5551.0000.147
FPN Flag0.0790.0150.0500.0030.0550.1950.1040.1010.0870.1040.0920.0260.0840.0820.2130.1870.1030.1030.1200.3211.0000.1470.0270.1471.000

Missing values

2023-06-05T09:52:11.131909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-05T09:52:16.542767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-05T09:52:23.968916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

recordtypesettlementdatesettlementdate_yearsettlementdate_monthsettlementdate_daysettlementperiodBMU IDacceptedpriceacceptedvolumeBMU TypeBMU Fuel TypeBMU GSP Group IdBMU GSP Group NameLOC LATLOC LONGGSP LOC CenterLOC Center LATLOC Center LONGBMU Party IDBMU Party NameTrading UnitPC FlagPC StatusTransmission Loss FactorGeneration CapacityDemand CapacityExempt Export FlagBase TU FlagFPN Flag
0BID2021-01-012021111E_GYAR-137.0-18.366ECCGT_AEastern England52.5838341.733725Grantham52.912504-0.643771RWE Generation UK plcINNOGY01NaNNaNProduction (P)0.004895420.0-18.0False (F)False (F)True (T)
1BID2021-01-012021111E_SHOS-135.0-0.666ECCGT_JSouth Eastern England50.829511-0.229161Leadhills55.469264-3.736325VPI Power LimitedSPGEN01NaNNaNProduction (P)0.012051436.0-16.0False (F)False (F)True (T)
2BID2021-01-012021111T_CDCL-140.0-11.084TCCGT_BEast Midlands53.307421-0.786058Milnthorpe54.264138-2.768073Uniper UK LimitedEECLNaNNaNProduction (P)0.005473445.0-12.0False (F)False (F)True (T)
3BID2021-01-012021111T_MRWD-137.1-174.768TCCGT_HSouthern England50.898831-1.437187Leadhills55.469264-3.736325Marchwood Power LimitedMPLNaNNaNProduction (P)0.017790920.0-15.0False (F)False (F)True (T)
4BID2021-01-012021111T_PEMB-1137.0-33.634TCCGT_KSouthern Wales51.683003-4.994865Newmarket52.2475470.394057RWE Generation UK plcINNOGY01Pembroke Power StationNaNProduction (P)0.009636475.0-11.0False (F)False (F)True (T)
5BID2021-01-012021111T_SCCL-36.0-49.000TCCGT_MYorkshire53.735187-0.243281Kingussie57.229009-4.078819SCCLSALTENDSALTEND1NaNProduction (P)-0.002454400.0-20.0False (F)False (F)True (T)
6BID2021-01-012021111T_SHBA-10.0-299.000TCCGT_MYorkshire53.602454-0.144829Kingussie57.229009-4.078819EP UK INVESTMENTS LIMITEDEPUKISOUTH HUMBER BANK P/S.NaNProduction (P)-0.002454810.0-10.0False (F)False (F)True (T)
7BID2021-01-012021112T_GRAI-640.0-31.312TCCGT_JSouth Eastern England51.4435460.707775Bromsgrove52.353379-2.062962Uniper UK LimitedEECLGRAIN PS TRADING UNITNaNProduction (P)0.012051468.0-14.0False (F)False (F)True (T)
8BID2021-01-012021112T_GRAI-740.0-4.666TCCGT_JSouth Eastern England51.4435460.707775Newmarket52.2475470.394057Uniper UK LimitedEECLGRAIN PS TRADING UNITNaNProduction (P)0.012051468.0-14.0False (F)False (F)True (T)
9BID2021-01-012021112T_MRWD-137.1-54.600TCCGT_HSouthern England50.898831-1.437187Leadhills55.469264-3.736325Marchwood Power LimitedMPLNaNNaNProduction (P)0.017790920.0-15.0False (F)False (F)True (T)
recordtypesettlementdatesettlementdate_yearsettlementdate_monthsettlementdate_daysettlementperiodBMU IDacceptedpriceacceptedvolumeBMU TypeBMU Fuel TypeBMU GSP Group IdBMU GSP Group NameLOC LATLOC LONGGSP LOC CenterLOC Center LATLOC Center LONGBMU Party IDBMU Party NameTrading UnitPC FlagPC StatusTransmission Loss FactorGeneration CapacityDemand CapacityExempt Export FlagBase TU FlagFPN Flag
1044174OFFER2023-03-012023317T_SEAB-1165.0476.000TCCGT_LSouth Western England51.539486-2.670153Kingussie57.229009-4.078819Seabank Power LimitedSEABANKSeabank Power StationNaNProduction (P)0.012720830.0-11.0False (F)False (F)True (T)
1044175OFFER2023-03-012023317T_STAY-4195.0195.000TCCGT_BEast Midlands53.075154-0.856134Milnthorpe54.264138-2.768073RWE Generation UK plcINNOGY01Staythorpe Power StationNaNProduction (P)0.005607464.0-15.0False (F)False (F)True (T)
1044176OFFER2023-03-012023318T_CNQPS-2159.0230.000TCCGT_DMerseyside and Northern Wales53.231614-3.081947Goole53.716633-0.873571Uniper UK LimitedEECLCONNAHS QUAY PS TRADING UNITNaNProduction (P)0.002671352.0-20.0False (F)False (F)True (T)
1044177OFFER2023-03-012023318T_SEAB-1165.0476.000TCCGT_LSouth Western England51.539486-2.670153Kingussie57.229009-4.078819Seabank Power LimitedSEABANKSeabank Power StationNaNProduction (P)0.012720830.0-11.0False (F)False (F)True (T)
1044178OFFER2023-03-012023318T_STAY-4195.0195.000TCCGT_BEast Midlands53.075154-0.856134Milnthorpe54.264138-2.768073RWE Generation UK plcINNOGY01Staythorpe Power StationNaNProduction (P)0.005607464.0-15.0False (F)False (F)True (T)
1044179OFFER2023-03-012023319T_CNQPS-2159.0230.000TCCGT_DMerseyside and Northern Wales53.231614-3.081947Goole53.716633-0.873571Uniper UK LimitedEECLCONNAHS QUAY PS TRADING UNITNaNProduction (P)0.002671352.0-20.0False (F)False (F)True (T)
1044180OFFER2023-03-012023319T_PEMB-11150.016.430TCCGT_KSouthern Wales51.683003-4.994865Newmarket52.2475470.394057RWE Generation UK plcINNOGY01Pembroke Power StationNaNProduction (P)0.005824475.0-11.0False (F)False (F)True (T)
1044181OFFER2023-03-012023319T_SEAB-1165.0476.000TCCGT_LSouth Western England51.539486-2.670153Kingussie57.229009-4.078819Seabank Power LimitedSEABANKSeabank Power StationNaNProduction (P)0.012720830.0-11.0False (F)False (F)True (T)
1044182OFFER2023-03-012023319T_SHBA-1200.00.432TCCGT_MYorkshire53.602454-0.144829Kingussie57.229009-4.078819EP UK INVESTMENTS LIMITEDEPUKISOUTH HUMBER BANK P/S.NaNProduction (P)-0.005257821.4-10.0False (F)False (F)True (T)
1044183OFFER2023-03-012023319T_STAY-4195.0195.000TCCGT_BEast Midlands53.075154-0.856134Milnthorpe54.264138-2.768073RWE Generation UK plcINNOGY01Staythorpe Power StationNaNProduction (P)0.005607464.0-15.0False (F)False (F)True (T)